It is well known that cardiac dysfunction in sepsis is associated with significantly increased mortality. The pathophysiology of sepsis-induced cardiac dysfunction can be summarized as involving impaired myocardial circulation, direct myocardial depression, and mitochondrial dysfunction. Impaired blood flow to the myocardium is associated with microvascular dysfunction, impaired endothelium, and ventriculo-arterial uncoupling. The mechanisms behind direct myocardial depression consist of downregulation of β-adrenoceptors and several myocardial suppressants (such as cytokine and nitric oxide). Recent research has highlighted that mitochondrial dysfunction, which results in energy depletion, is a major factor in sepsis-induced cardiac dysfunction. Therefore, the authors summarize the pathophysiological process of cardiac dysfunction in sepsis based on the results of recent studies.
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Acute Crit Care. 2020;35(2):67-76. Published online May 15, 2020
Background Sepsis-induced cardiomyopathy (SIC) occurs frequently in critically ill patients, but the clinical features and prognostic impact of SIC on sepsis outcome remain controversial. Here, we investigated the predictors and outcomes of SIC.
Methods Patients admitted to a single medical intensive care unit from June 2016 to September 2017 were retrospectively reviewed. SIC was diagnosed by ejection fraction (EF) <50% and ≥10% decrease in baseline EF that recovered within 2 weeks.
Results In total, 342 patients with sepsis met the inclusion criteria, and 49 patients (14.3%) were diagnosed with SIC; the latter were compared with 259 patients whose EF was not deteriorated by sepsis (non-SIC). Low systolic blood pressure and increased left ventricular end-diastolic diameter (LVEDD) were identified as predictors of SIC. SIC and non-SIC patients did not differ significantly in terms of 28-day all-cause mortality (24.5% vs. 26.3%, P=0.936). Acute Physiology and Chronic Health Evaluation II (APACHE II; hazard ratio [HR], 1.10; 95% confidential interval [CI], 1.02 to 1.18; P=0.009) and delta neutrophil index (DNI; HR, 1.02; 95% CI, 1.00 to 1.08; P=0.026) were independent risk factors for 28-day mortality with SIC. DNI, APACHE II, and lactate were identified as risk factors for 28-day mortality in sepsis patients as a whole.
Conclusions SIC was not associated with increased mortality compared to non-SIC. Low systolic blood pressure and increased LVEDD were predictors of SIC. High APACHE II score and elevated DNI, which reflect sepsis severity, predict 28-day all-cause mortality.
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Background Clinical deteriorations during hospitalization are often preventable with a rapid response system (RRS). We aimed to investigate the effectiveness of a daytime RRS for surgical hospitalized patients.
Methods A retrospective cohort study was conducted in 20 general surgical wards at a 1,779-bed University hospital from August 2013 to July 2017 (August 2013 to July 2015, pre-RRS-period; August 2015 to July 2017, post-RRS-period). The primary outcome was incidence of cardiopulmonary arrest (CPA) when the RRS was operating. The secondary outcomes were the incidence of total and preventable cardiopulmonary arrest, in-hospital mortality, the percentage of “do not resuscitate” orders, and the survival of discharged CPA patients.
Results The relative risk (RR) of CPA per 1,000 admissions during RRS operational hours (weekdays from 7 AM to 7 PM) in the post-RRS-period compared to the pre-RRS-period was 0.53 (95% confidence interval [CI], 0.25 to 1.13; P=0.099) and the RR of total CPA regardless of RRS operating hours was 0.76 (95% CI, 0.46 to 1.28; P=0.301). The preventable CPA after RRS implementation was significantly lower than that before RRS implementation (RR, 0.31; 95% CI, 0.11 to 0.88; P=0.028). There were no statistical differences in in-hospital mortality and the survival rate of patients with in-hospital cardiac arrest. Do-not-resuscitate decisions significantly increased during after RRS implementation periods compared to pre-RRS periods (RR, 1.91; 95% CI, 1.40 to 2.59; P<0.001).
Conclusions The day-time implementation of the RRS did not significantly reduce the rate of CPA whereas the system effectively reduced the rate of preventable CPA during periods when the system was operating.
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Background There are few studies on the effect of intensivist staffing in pediatric intensive care units (PICUs) in Korea. We aimed to evaluate the effect of pediatric intensivist staffing on treatment outcomes in a Korean hospital PICU.
Methods We analyzed two time periods according to pediatric intensivist staffing: period 1, between November 2015 to January 2017 (no intensivist staffing, n=97) and period 2, between February 2017 to February 2018 (intensivists staffing, n=135).
Results Median age at admission was 5.4 years (range, 0.7–10.3 years) in period 1 and 3.6 years (0.2–5.1 years) in period 2 (P=0.013). The bed occupancy rate decreased in period 2 (75%; 73%–88%) compared to period 1 (89%; 81%–94%; P=0.015). However, the monthly bed turnover rate increased in period 2 (2.2%; 1.9%–2.7%) compared to period 1 (1.5%, 1.1%– 1.7%; P=0.005). In both periods, patients with chronic neurologic illness were the most common. Patients with cardiovascular problems were more prevalent in period 2 than period 1 (P=0.008). Daytime admission occurred more frequently in period 2 than period 1 (63% vs. 39%, P<0.001). The length of PICU stay, parameters related with mechanical ventilation and tracheostomy, and pediatric Sequential Organ Failure Assessment score were not different between periods. Sudden cardiopulmonary resuscitations occurred in two cases during period 1, but no case occurred during period 2.
Conclusions Pediatric intensivist staffing in the PICU may affect efficient ICU operations.
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Background Mean systemic filling pressure (Pmsf) is a quantitative measurement of a patient’s volume status and represents the tone of the venous reservoir. The aim of this study was to estimate Pmsf after severe hemorrhagic shock and cardiac arrest in swine anesthetized with propofol-based total intravenous anesthesia, as well as to evaluate Pmsf’s association with vasopressor-free resuscitation.
Methods Ten healthy Landrace/Large-White piglets aged 10–12 weeks with average weight 20±1 kg were used in this study. The protocol was divided into four distinct phases: stabilization, hemorrhagic, cardiac arrest, and resuscitation phases. We measured Pmsf at 5–7.5 seconds after the onset of cardiac arrest and then every 10 seconds until 1 minute postcardiac arrest. During resuscitation, lactated Ringers was infused at a rate that aimed for a mean right atrial pressure of ≤4 mm Hg. No vasopressors were used.
Results The mean volume of blood removed was 860±20 ml (blood loss, ~61%) and the bleeding time was 43.2±2 minutes while all animals developed pulseless electrical activity. Mean Pmsf was 4.09±1.22 mm Hg, and no significant differences in Pmsf were found until 1 minute postcardiac arrest (4.20±0.22 mm Hg at 5–7.5 seconds and 3.72±0.23 mm Hg at 55– 57.5 seconds; P=0.102). All animals achieved return of spontaneous circulation (ROSC), with mean time to ROSC being 6.1±1.7 minutes and mean administered volume being 394±20 ml.
Conclusions For the first time, Pmsf was estimated after severe hemorrhagic shock. In this study, Pmsf remained stable during the first minute post-arrest. All animals achieved ROSC with goal-directed fluid resuscitation and no vasopressors.
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Methods We examined blunt trauma cases that were admitted to our hospital during 2010– 2014. Eligibility criteria were cases with an Injury Severity Score of ≥9, complete Trauma and Injury Severity Score (TRISS) variable data, and lactate levels that were assessed upon ER arrival. Survivor and non-survivor groups were compared and lactate-based prediction models were generated using logistic regression. We compared the predictive performances of traditional prediction models (Revised Trauma Score [RTS] and TRISS) and lactate-based models using the area under the curve (AUC) of receiver operating characteristic curves.
Results We included 829 patients, and the in-hospital mortality rate among these patients was 21.6%. The model that used lactate levels and age provided a significantly better AUC value than the RTS model. The model with lactate added to the TRISS variables provided the highest Youden J statistic, with 86.0% sensitivity and 70.8% specificity at a cutoff value of 0.15, as well as the highest predictive value, with a significantly higher AUC than the TRISS.
Conclusions These findings indicate that lactate testing upon ER arrival may help supplement or replace traditional physiological parameters to predict mortality outcomes among Korean trauma patients. Adding lactate levels also appears to improve the predictive abilities of existing trauma outcome prediction models.
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